Reproducing Context-sensitive Crashes in Mobile Apps using Crowdsourced Debugging

Maria Gomez 1 Romain Rouvoy 1 Lionel Seinturier 1
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : While the number of mobile apps published by app stores keeps increasing, the quality of these apps greatly varies. Unfortunately, end-users continue experiencing bugs and crashes for some of the apps installed on their mobile devices. Although developers heavily test their apps before release, context-sensitive crashes might still emerge after deployment. This paper therefore introduces MoTiF, a crowdsourced approach to support developers in automatically reproducing context-sensitive crashes faced by end-users in the wild. The goal of MoTiF is to complement existing testing solutions with mechanisms to monitor and debug apps after their deployment. We demonstrate that MoTiF can effectively reproduce existing crashes in Android apps with a low overhead.
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[Research Report] RR-8731, Inria Lille; INRIA. 2015
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Maria Gomez, Romain Rouvoy, Lionel Seinturier. Reproducing Context-sensitive Crashes in Mobile Apps using Crowdsourced Debugging. [Research Report] RR-8731, Inria Lille; INRIA. 2015. 〈hal-01155597v2〉

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